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https://www.northeastern.edu/graduate/blog/what-does-a-data-analyst-do/

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What Does a Data Analyst Do: Responsibilities, Skills, and Salary

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There will be more than 2.7 million job openings in data analytics by 2020. Learn more about the role of a data analyst and the skills employers need to fill these jobs.

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What Does a Data Analyst Do?

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3. What does a data analyst do ( scrip )

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3. What does a data analyst do ( scrip )

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Base on the video and the documents I've found in the web, now i'll show you what a data analyst extrally do.
Firstly, i'll show you what a data analyst do in general.  As same as other people, they also have free time, working, family, ... . But the job of them contracts to many companies. They always improving, retrieving and gathering data, organizing it and using it to reach meaningful conclusions. Regardless of which industry they work in, data analysts can expect to spend their time developing systems for collecting data and compiling their findings into reports that can help improve their company. If you were an data analyst in some companies, you might even be asked to train others in your data-collection system.
So, now we'll dig into the specifics of life on the job as a data analyst. If you're thinking about becoming a data analyst, you'll be interested in the roles and responsibilities that you'll have in field based on some real job and here are some roles and responsibilities that you'll have to undertake as a data analyst:
+) Manage the delivery of user satisfaction surveys and report on results using data visualization software
+) Working with business line owners to develop requirement, define success metrics, manage and execute analytical projects and evaluate results
+) Monitors practices, processes, and systems to identify opportunities for improvement
+) Translate important questions into concreate analytical tasks
+) Gather new data to answer client questions, collating and organizing data from multiple sources
+) Design, build, test and maintain back-end code
+) Establish data processes, define data quality criteria, and implement data quality processes
... etc
So that is just some expamles of what a data analyst would be doing.

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https://www.youtube.com/watch?v=yZvFH7B6gKI

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What Is Data Analytics? - An Introduction (Full Guide)

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https://www.mastersindatascience.org/learning/what-is-data-analytics/

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As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question "what happened?" This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses "what if?" questions.

The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has been made possible by advances in parallel processing and cheap computational power.

Types of Data Analytics

Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business.

Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance.

Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps:

Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.

Data that is related to these anomalies is collected.

Statistical techniques are used to find relationships and trends that explain these anomalies.

Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.

Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated.

These types of data analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination they provide a well-rounded understanding of a company's needs and opportunities.

What is the Role of Data Analytics?

Data analysts exist at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organizations succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data.

Thinking about a graduate degree in data analytics? Start with a sponsored online analytics program:

The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an essential process for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases, or raw sensor data. The key steps in this process are to extract, transform, and load data (often called ETL.) These steps convert raw data into a useful and manageable format. This prepares data for storage and analysis. Data mining is generally the most time-intensive step in the data analysis pipeline.

Data management or data warehousing is another key aspect of a data analyst's job. Data warehousing involves designing and implementing databases that allow easy access to the results of data mining. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are becoming more common as well.

Statistical analysis allows analysts to create insights from data. Both statistics and machine learning techniques are used to analyze data. Big data is used to create statistical models that reveal trends in data. These models can then be applied to new data to make predictions and inform decision making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open source libraries and packages such as TensorFlow enable advanced analysis.

The final step in most data analytics processes is data presentation. This step allows insights to be shared with stakeholders. Data visualization is often the most important tool in data presentation. Compelling visualizations can help tell the story in the data which may help executives and managers understand the importance of these insights.

Why Data Analytics is Important?

The applications of data analytics are broad. Analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world.

One of the earliest adopters is the financial sector. Data analytics has an important role in the banking and finance industries, used to predict market trends and assess risk. Credit scores are an example of data analytics that affects everyone. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.

The use of data analytics goes beyond maximizing profits and ROI, however. Data analytics can provide critical information for healthcare (health informatics), crime prevention, and environmental protection. These applications of data analytics use these techniques to improve our world.

Though statistics and data analysis have always been used in scientific research, advanced analytic techniques and big data allow for many new insights. These techniques can find trends in complex systems. Researchers are currently using machine learning to protect wildlife.

The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding and improving diagnostic techniques are just a few examples of how data analytics is revolutionizing healthcare. The pharmaceutical industry is also being revolutionized by machine learning. Drug discovery is a complex task with many variables. Machine learning can greatly improve drug discovery. Pharmaceutical companies also use data analytics to understand the market for drugs and predict their sales.

The internet of things (IoT) is a field that is used alongside machine learning. These devices provide a great opportunity for data analytics. IoT devices often contain many sensors that collect meaningful data points for their operation. Devices like the Nest thermostat track movement and temperature to regulate heating and cooling. Smart devices like this can use data to learn from and predict your behavior. This will provide advance home automation that can adapt to the way you live.

The applications of data analytics are seemingly endless. More and more data is being collected every day - this presents new opportunities to apply data analytics to more parts of business, science and everyday life.

Data Analytics FAQ

What is the role of data analytics?

Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.

What are the types of data analytics?

There are various types of data analysis including descriptive, diagnostic, prescriptive and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.

What are the analytical tools used in data analytics?

There are various tools used in data analysis. Some data analysts use business intelligence software, such as Tableau. Others may use programming languages such as SQL or Python, which have various statistical and visualization libraries.

What is the career growth in data analytics?

According to O*NET, the projected growth for data analysts is 8% between 2019-2029. On average, data analysts earned $94,280 in 2019. However, salary compensation for data analysts varies depending on where they work and what industry they work in.

Last updated: February 2021

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As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals. The data analytics process has some components that can help a variety of initiatives.

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https://www.rasmussen.edu/degrees/technology/blog/what-does-a-data-analyst-do/

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What Does a Data Analyst Do? Exploring the Day-to-Day of This Tech Career

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Jobs for data analysts are on the rise. But what does this position actually entail? Join us as we take a behind-the-scenes look at this up-and-coming tech career.

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https://www.youtube.com/watch?v=ywZXpfdqg1o

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2.What are data analytics used for (Dũng)

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2.What are data analytics used for (Dũng)

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5.The skill needed to become a data analyst (Hoàng)

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5.The skill needed to become a data analyst (Hoàng)

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https://www.digitalvidya.com/blog/data-analytics-skills/

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The world has evolved so much that people have moved from being superstitious to being logical. Unlike before, people now correlate events and occurrences rationally to ascertain the cause of problems and proffer possible solutions.

Therefore, Data Analytics refers to a method in which data is collected and organized in such a manner that meaningful and helpful information can be derived from it. With the u

In other words, the main reason behind data analysis is to examine the information embedded in any data. This requires having data analytics skills.

Looking at it from another perspective, data analysis refers to the process of cross-checking, cleaning, reorganizing and modelling data for decision making in business. Data gotten from different sources are put together, examined and then analyzed to arrive at a conclusion.

Therefore, a data analyst gathers, arranges, processes and model data. He is capable of analyzing a large volume of data, be it structured or unstructured. At the end of the day, the main goal of data analysis is to prepare and present data in the right form (graphs, charts or tables) for decision-making and problem-solving process.

To analyze means to break down a whole into its components in order to examine each of them individually and generate an explanation for why it is the way it is.

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Over the years, people who are vast in mathematical and programming abilities have been getting handsomely rewarded. This is because these two abilities form the basis on which data analysis operates. As more and more companies generate huge data, new industries have joined in craving for these data analyst skill set, especially in the technology sector.

Skills Gap in Companies today

In recent times, employees seem to have little understanding of their companies' data. Aside from having enough data analysts, most companies have surprisingly few people who can analyze their data in the most basic manners.

This trend may be blamed partly on the system companies have in place. Many businesses, especially those that grew through acquisition and inherited several IT departments, save their data in systems that are difficult for non-technical employees to access or use.

That discourages the majority of people from going anywhere near their company's raw data. What is even the bigger obstacle is that, as much as decent tools may be available, it takes technical know-how and patience, which most people do not have, to use them?

Data analysts are in high demand in virtually all sectors including consulting, government, pharmaceuticals, manufacturing, finance and education. If you are curious, mathematically skilled, and analytical, then you should consider a career in data analytics.

Some of the areas where you can work as a data analyst include:

Responsibilities of a Data Analyst

Some major responsibilities you would be carrying out when employed as a data analyst are:

1. You would identify areas where your company needs to improve in efficiency and process automation

2. You would come up with records of the management policies and processes

3. You would initiate and maintain processes of automated data

4. You would develop and support various reporting processes.

5. You would monitor and audit the quality of data

6. You would identify, evaluate and implement services and tools from external sources to support validation of data and cleansing.

7. You would work with both internal and external clients to adequately understand data content.

8. You would Create dashboards, graphs and visualizations on data.

9. From time to time, you would prepare reports for both internal and external audiences using various business analytics reporting tools.

10. Design, conduct and analyze survey data.

Skills Required for Data Analyst

If you intend to become a data analyst, you must start by ensuring you get a good background in mathematics, technology, business intelligence, data mining and statistics. You also need to possess a couple of data analytics skills which include:

As the name implies, it is clear that data analytics skills are of huge importance in data analysis. These data analytics skills refer to the ability to gather, view and analyze all forms of information in details. They also mean the ability to view a challenge or situation from different perspectives.

Data Analytics skills are major data analyst skills that make it possible for you to address problems by making decisions in the most appropriate way. Hence, if you wish to become a successful data analyst, you need to acquire and improve your data analytics skills and thinking.

2.) Numeracy Skills

These refer to mathematical skills which include a wide range of abilities including:

1. Have good knowledge of figures and numbers

2. Understand the relationships existing between numbers

Interpret any mathematical information

1. Have the ability to organize information

2. Be able to schedule or budget

3. Be conversant with trends

4. Know how to measure and analyze data

5. Be able to work with graphical information

6. Possess calculation skills

3.) Technical and Computer Skills

Good computer and data analyst technical skills are among the important data analytics skills. You should possess a basic knowledge of statistics. Also, you need to be familiar with some computer software and tools including; scripting language (Matlab, Python), Querying Language (SQL, Hive, Pig), Spreadsheet (Excel) and Statistical Language (SAS, R, SPSS). Other computer skills include; programming (Javascript, XML), big data tools (Spark, Hive HQL) and so on.

These are also important data analytics tools that any intending data analyst must develop. Ability to pay attention to details allows a data analyst find and see initially unseen details and links. This ability is especially important at the point of solving problems and making decisions. One who pays attention to details to work better and stands a lower risk of making errors.

Employers do not joke with this strength, this explains why many people include attention to details ability in their résumés and job applications.

5.) Business Skills

You also need to possess certain business skills to function well as a data analyst. The major business skills you need include:

Decision-making and problem-solving skills: These two work together. This is the main crux of data analysis as data analytics jobs are to give the right information for decision-making and problem-solving process.

The ability to acquire knowledge of new things, especially with regards to technological developments.

Communication skills: As a data analyst, it is expected of you to work with different people in your team. You should be able to communicate effectively with directors and co-workers to prepare, present and explain data.

Time management and organization skills: Your ability to manage time effectively will enable you to handle your responsibilities and meet deadlines.

6.) Communication Skills

Data Analytics job requires you to be an excellent communicator. In other words, you must be able to facilitate meetings, make the right requests and be an active listener in order to assimilate new information. Your communication proficiency should also cut across different digital platforms such as the internet, conference calls, SMS among others. The nature of data analytics jobs requires you to spend a significant amount of time relating to management, users, developers and clients. You really do not want to disappoint any of these categories of people.

Aside all of the above, some experts have argued that essentially there are 7 data analytics skills required. The data analyst qualifications include:

1. Campaign Management (Adobe Campaign): To become a successful data analyst, you need to get a good grasp of this software. It allows you to manage your data and makes it possible for you to monitor your cross-channel campaigns from a particular place. Don't just get familiar with this important tool, get confident with it.

2. Analytical and Creative Mind: You need to show that you are passionate about the industry and that you possess a creative and analytical mind for anyone to take you seriously. Go ahead and create campaigns and projects of your own to showcase your creativity.

3. Advanced Microsoft Excel: You cannot separate Excel form data analysis as it plays a very important role in the process. Make sure you know your way around the numerous functions available on Excel to be successful in data analysis profession.

4. The Data and analytics industry is not different from other industries when it comes to preferring with relevant experience of working with people in a team over those who don't. In as much as the majority of data analytic work may be done independently, you still need a team to execute certain projects. Of course, you need to clearly spell this out in your curriculum vitae.

5. You need to know such scripting and analytical languages like Python, Matlab, R and Scripting and Statistical Language: SAS as a data analyst. The more of these languages you know, the more you can include in your CV, thereby increasing your chances of landing your dream job.

6. Though Adobe and Google Analytics: Google Analytics is the most popular analytical tool, Adobe Analytics is also very useful in powerful analytics. Acquiring an advanced understanding of these tools will definitely boost your chances of beating competitions in the market.

7. SQL is a very important language when it comes to data analysisStructured Query Language (SQL) Programming: . Most employers consider it to be among the most valuable data analytics skills. However, only very few data analysts possess this skill. Try and become skilled in this area and watch you beat your competitors to secure data analytics jobs. Though it may not be needed for some analysis, it is always important to possess this skill.

After acquiring the above-highlighted Data analytics skills through data analyst certification, you can use them to attract attention to yourself throughout your job search. To start with, state the names of the skills as keywords when putting your resume together. Do not assume or conclude that the Human Resource Personnel would figure out that you possess the data analytics skills they want.

When you see a job you want to apply for, carefully go through the job description and conduct a couple of research on the company. This way, you would be able to tell what skills the company prioritizes; then you can go ahead and highlight them in your cover letter (provided you possess the skills). In a situation whereby you don't possess all the skills the company need, feel free to mention as many as you possess, including the ones not mentioned but relevant to data analytics jobs.

You may be asked to give instances of situations where your skills turned out very useful, so be prepared to describe at least a scenario in which your skills saved the day.

A first degree is often required, not all the time though. To work as a data analyst, a degree in any of these relevant disciplines may go a long way:

As much as the above degrees are important to your success as a data analyst, what matters most is the data analytics skills you possess and this is why you should look forward to a data analyst certification.

These days, a postgraduate degree in data science is becoming more widespread among data analysts (or intending data analysts). You may find this academic qualification (postgraduate degree) helpful if you are changing career or you want to learn data analysis skills. Some of the subjects in which postgraduate qualifications are offered include MSc Business Analytics, MSc Big Data, MSc Data Science, MSc Data Science and Analytics.

There are also a couple of data analysis skills you can learn through data analyst certification which is available at universities and specialist data schools including:

Data analysis is a rapidly growing field and highly skilled analysts are in increased demand across all sectors. This is evident from the average salary of a data analyst in India.

However, this gives you a compelling reason to take up a data analyst certification.

This implies that you would find many opportunities but you will still have to be outstanding and exhibit excellent data analytics skills to be successful as a data analyst.

Data Analysis maybe a broad concept, a list of skills if acquired will make you excel in the field. Learning them is now easy with a full set of this Data Analytics Using Excel Course.

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Top Data Analytics Skills Required To Become A Data Analyst

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The world has evolved so much that people have moved from being superstitious to being logical. Unlike before, people now correlate events and occurrences rationally to ascertain the cause of problems and proffer possible solutions. Therefore, Data Analytics refers to a method in which data is collected and organized in such a manner that meaningful and helpful information can be derived from it.

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5 steps :
+) Define the questions
+) Collect the data
+) Clean the data
+) Analyze the data
+) Interpret and share the result

Scrip :
+) Step 1:
At this stage data analysts will take clearly defined problem and then they will make a hypothesis or research question that they can go on and answer. Then they need to identify what types of data they'll need but importantly where it will come from .
+) Step 2:
Data analysts will usually gather data from primary sources or internal data that the company already has. They may tend to secondary or external sources such as open data sources. So this can include not only data from government portals tools like google trends but also from international organization like The World Heath Organization.
+) Step 3:
Your original data might include duplicates anomalies or missing data which could distort how the data is interpreted. These all need to be removed. Data cleaning can be a manual task and it can take some time but it's crucial if you want to get the right business.
+) Step 4:
Data analysts analyze the data depending on the question that they are asking and the kind of data that they are working with.
+) Step 5:
Depending on the type of analysis conducted, data analysts will present their findings in a way that others can understand in the form of chart or graph for example

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5.The skill needed to become a data analyst (scrip)

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5.The skill needed to become a data analyst (scrip)

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Based on the documents I have read, I will tell you the skills required to become a data analyst. First of all, it must be an analytical skill. As the name implies, it is clear that data analytics skills are of huge importance in data analysis. Data Analytics is a key skill of a data analyst that enables you to solve problems by making decisions in the most appropriate way. That's why data analysis skills and mindsets are so important. As for data cleaning and preparation, studies show that it accounts for about 80% of the work of data analysts. That's probably also why it's such an important skill for anyone in the field. Next, a strong foundation in probability and statistics is an important data analyst skill. It will help you navigate your data analysis as well as help you avoid common mistakes. To be able to do this job you also need to have technical and computer skills. You need to be familiar with some software and computer tools in terms of languages and programming. Another skill to serve in data analysis for different projects is domain knowledge. Domain knowledge changes from industry to industry, so you may find yourself needing to research and learn quickly. No matter what you're working on you need to understand what you're analyzing, so domain knowledge becomes a really essential skill for a data analyst. Finally, in my opinion, communication skills as well as problem solving are also particularly important skills. As an analyst you face problems every day, you need to be able to solve them, make appropriate inquiries and listen intelligently to others. Good communication skills can also help you advance faster at work. Those are the skills required to become a data analyst as far as I know...

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5.The skill needed to become a data analyst (Hoàng)

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5.The skill needed to become a data analyst (Hoàng)

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https://www.dataquest.io/blog/data-analyst-skills/